Ever heard of ‘synthetic data’? Synthetic data is data that is artificially created (from statistical models), rather than generated by actual events. It contains all the characteristics of production data, minus the sensitive stuff. By 2024, 60% of the data used for the development of AI and analytics projects will be synthetically generated, according to Gartner. The reason organisations may use synthetic data over actual data is because you can get it more quickly, easily and cheaply. But there are concerns with this approach, because synthetic data is based on models and algorithms designed by humans and their biases. More data doesn’t necessarily equal better data. Is synthetic data a brilliant tool for improving data quality, reducing data acquisition costs, managing privacy and reducing overfitting? Or does synthetic data put us on a slippery slope of hard-to-interrogate models that are technically replacing fact with fiction? To answer these questions, I recently spoke to Minhaaj Rehman, who is CEO & Chief Data Scientist at Psyda, an AI-enabled academic and industrial research agency. In this episode of Leaders of Analytics, you will learn: What synthetic data is and how it is generatedThe most common uses for synthetic dataThe arguments for and against using synthetic dataWhen synthetic data is most helpful and when it is most riskyHow to implement best practices for mitigating the risks associated with synthetic data, and much more.Episode timestamps: 00:00 Intro 03:00 What Psyda Does 04:23 Academic Work and Modern Education 06:38 Getting into Data Science 11:30 What is Synthetic Data 13:30 Common Applications for Synthetic Data 18:50 Pros & Cons of using Synthetic Data 21:29 Risks of using Synthetic Data 23:48 When should Synthetic Data be Used 29:23 Synthetic Data is Cleaner than Real Data 34:05 Using Synthetic Data for Risk Mitigation 36:05 Resources on Learning More about Synthetic Data 38:05 Human Biases in Decision Making   Connect with Minhaaj: Minhaaj on LinkedIn: https://www.linkedin.com/in/minhaaj/ Minhaaj's website and podcast: https://minhaaj.com/

Data science and machine learning are continuing to evolve as core capabilities across many industries. But high-quality data science output is only half the story. As the data science profession matures from “back office support” to leading from the front, there is an increasing need for more integrated systems that plug into business operations. To get the most out of these capabilities, organisations must move beyond just building robust models, and establish operational processes that can produce, implement and maintain machine learning systems at scale. Enter MLOps. To understand the fundamentals and best practices of MLOps, I recently spoke to Shalini Kurapati who is CEO of Clearbox.ai. Clearbox AI is the data-centric MLOps company that enables trustworthy and human-centred AI. Their AI Control Room automatically produces synthetic data and insights to solve the issues related to data quality, data access and sharing, and privacy aspects that block AI adoption in companies. In this episode of Leaders of Analytics, we cover: What MLOps is and why we need it to succeed with advanced data science solutionsHow to get beyond the proof-of-concept-to-production gap and get models into operationThe importance of data-centric AI in building MLOps best practicesThe most common AI pitfalls to avoidHow Human Centred Design principles can be used to build AI for good, and much more.Check out Clearbox here: https://clearbox.ai/ Connect with Shalini here: https://www.linkedin.com/in/shalini-kurapati-phd-she-her-06516324/